Can Large Language Models facilitate network configuration? In our recently accepted CoNEXT 2024 paper, we investigate the opportunities and challenges in operating network systems using recent LLM models.

We devise a benchmark for evaluating the capabilities of different LLM models on a variety of networking tasks and show different ways of integrating such models within existing systems. Our results show that different models works better in different tasks. Translating high-level human-language requirements into formal specifications (e.g., API function calling) can be done with small models. However, generating code that controls network systems is only doable with larger LLMs, such as GPT4.

This is a first fundamental first step in our SEMLA project looking at ways to integrate LLMs into system development.

GitHub code: link

Hugging Face: link

Paper PDF: link

Two project proposals led by Marco Chiesa and Dejan Kostić have recently been funded by Vetenskaprådet (VR, Swedish Research Council in English).

The first project is a Starting Grant with a single PI (Marco Chiesa), titled “ResoNet: Resilient Optimized Network Synthesis”, and funded with 4M SEK and running between 2022 and 2025. The project aims at developing new network synthesis methods that guarantee performance and robustness requirements.

The second project is a Project Grant with Dejan Kostić as PI, titled “Scalable Federated Learning”, and funded with 3.8M SEK. This project is a collaboration with three more co-PIs: Magnus Boman (KTH), Marco Chiesa (KTH), and Sabine Koch (KI). The project will allow our group to explore a new research direction and, more specifically, we aim to develop a highly scalable, flexible, extensible, distributed federated machine learning approach that can directly benefit public health and wellness.

See a list of all funded VR projects here.